1. Field of the Invention
The present invention relates to an adaptive equalizer and an adaptive equalization scheme, and particularly to an adaptive equalizer applicable to digital radio communication equipment in digital mobile communication, digital satellite communication, digital mobile-satellite communication and the like.
2. Description of Related Art
In digital mobile communication, fading--variations in the amplitude and phase of a received signal--can occur because of reflection, diffraction or scattering of radio waves due to geography and terrestrial materials around a mobile station. In particular, when the delay time of delay waves cannot be neglected as compared with a symbol length, the spectrum of a signal is distorted, resulting in large degradation.
Such fading is called frequency selective fading because the spectral distortion has frequency dependency. An adaptive equalizer is one of conventional effective techniques to overcome such fading.
As configurations of conventional adaptive equalizers, are known a decision feedback equalizer (referred to as DFE from now on) that eliminates the effect of delay waves by feeding back decision results, and a maximum likelihood sequence estimation (referred to as MLSE from now on) that selects a maximum likelihood sequence from among all the sequences having possibilities to be transmitted.
Although the MLSE is a little larger in size than the DFE, it has better performance than the DEE because it can utilize the power of the delay waves.
As for fading resulting from fast time variations in channel characteristics, the adaptive MLSE is more advantageous which carries out tracking following variations in the channel characteristics not only during training period that obtains channel impulse responses (called CIR from now on) from a known training sequence, but also during data sections.
In particular, the MLSE that carries out channel estimation for respective states of Viterbi algorithm (referred to as per-survivor processing MLSE from now on) exhibits good performance even for fast time-varying channels by carrying out the CIR estimation for respective states of the MLSE.
A configuration of a per-survivor processing MLSE will be described here as a typical conventional adaptive equalizer.
FIG. 1 is a block diagram showing a configuration of a conventional per-survivor processing MLSE disclosed in H. Kubo, K. Murakami and T. Fujino, "An Adaptive Maximum-Likelihood Sequence Estimator for Fast Time-Varying Intersymbol Interference Channels", IEEE Transactions on Communications, Vol. 42, Nos. 2/3/4, 1994, pp. 1872-1880 (called REF. 1 below). In this figure, the reference numeral 11 designates a maximum likelihood sequence estimating section; 12a-12n each designate a CIR estimator; 101 designates a received baseband signal; 102 designates estimated CIRs of respective states; 103 designates tentative decisions of respective states; and 104 designates hard decision data.
Next, the operation of the conventional device will be described.
The maximum likelihood sequence estimating section 11, receiving the received baseband signal 101 and estimated CIRs of respective states 102, estimates a transmitted sequence by Viterbi algorithm, and outputs its results as hard decision data 104.
FIG. 2 is a block diagram showing an internal configuration of the maximum likelihood sequence estimating section 11. In FIG. 2, the reference numeral 21 designates a branch metric generator; 22 designates an ACS (add-compare-select) operation circuit; 23 designates a path metric memory; 24 designates a path memory; 20115 designates branch metrics; 202 designates path metrics; 203 designates path metrics at previous timing; and 204 designates a survivor path.
In the maximum likelihood sequence estimating section 11 within the conventional per-survivor processing MLSE with the foregoing configuration, a state s.sub.k and a path connected to a branch s.sub.k /s.sub.k-1 at time k of the Viterbi algorithm are defined by the following expressions (1) and (2). EQU s.sub.k =[I.sub.k, I.sub.k-1 . . . , I.sub.k-V+1 ] (1) EQU s.sub.k /s.sub.k-1 =[I.sub.k,I.sub.k-1 , . . . , I.sub.k-V ] (2)
where, I.sub.k is a candidates for the transmitted sequence determined by the state s.sub.k or by the branch s.sub.k /s.sub.k-1.
The branch metric generator 21 compares replicas of the received signal obtained from the estimated CIRs of respective states 102 with the received baseband signal 101, generates branch metrics 201 for all the branch candidates s.sub.k /s.sub.k-1, and supplies them to the ACS operation circuit 23.
Assuming that a metric criteria is a squared Euclidean distance, the branch metrics 201 can be expressed by the following expressions (3) and (4). EQU .GAMMA..sub.k [s.sub.k /s.sub.k-1 ]=.vertline.r.sub.k r.sub.k [s.sub.k /s.sub.k-1 ].vertline..sup.2 (3)
##EQU1##
where, .GAMMA..sub.k [s.sub.k /s.sub.k-1 ] is a branch metric 201 of the branch s.sub.k /s.sub.k-1, r.sub.k is the received baseband signal 101, r.sub.k [s.sub.k /s.sub.k-1 ] is a replica of the received signal determined by the branch s.sub.k /s.sub.k-1, c.sub.i,k [s.sub.k-1 ] is an estimated CIR 102 at the state s.sub.k-1, and L is a channel memory length. The branch 15 metric generator 21 also outputs candidates (I.sub.k, I.sub.k-1, . . . ,I.sub.k-V+1) of the transmitted sequence determined by the state s.sub.k as the tentative decisions of respective states 103 to be supplied to the CIR estimators 12a-12n.
The ACS operation circuit 22 adds the branch metrics 201 to the path metrics at previous timing 203 stored in the path metric memory 23 as the following expression (5) to obtain path metric candidates for all the branch candidates s.sub.k /s.sub.k-1. EQU H.sub.k [s.sub.k /s.sub.k-1 ]=H.sub.k-1 [s.sub.k-1 ]+.GAMMA..sub.k [s.sub.k /s.sub.k-1 ] (5)
where H.sub.k [s.sub.k /s.sub.k-1 ] is the path metric candidate determined by the branch s.sub.k /s.sub.k-1, and H.sub.k-1 [s.sub.k-1 ] is a path metric at previous timing 203 determined by the state s.sub.k-1. In addition, the ACS operation circuit 22 compares the path metric candidates H.sub.k [s.sub.k /s.sub.k -1 ] for each state s.sub.k as the following expression (6) to select a minimum path metric and supplies the minimum path metrics thus obtained to the path metric memory 23 as the path metrics 202. ##EQU2##
where, H.sub.k [s.sub.k ] is the path metric 202 determined by the state s.sub.k. The ACS operation circuit 22 also supplies the path memory 24 with the information on the selected path as the survivor path 204.
The path memory 25 stores the survivor paths 204 for a predetermined time period, traces the paths whose path metrics at previous timing 203 are minimum, and outputs the transmitted sequence determined by the paths as the hard decision data 104.
Each of the CIR estimators 12a-12n which are prepared by the number of the states of the maximum likelihood sequence estimating section 11, receives the received baseband signal 101 and the tentative decision of respective states 103, estimates the CIR for respective states using the LMS (least mean square) algorithm, and outputs the estimated CIR of respective states 102. Specifically, as the following expression (7), the CIR estimators 12a-12n update all the estimated CIRs for all the states s.sub.k and channels i(i=0, . . . ,L) to be output as the estimated CIRs of respective states 102. EQU c.sub.i,k+ [s.sub.k ]=c.sub.i,k [s.sub.k-1 :s.sub.k.sup.sv ].delta.(r.sub.k c.sub.i,k [s.sub.k-1 :s.sub.k.sup.sv ]I.sub.k-i I*.sub.k-i (7)
where, c.sub.i,k+1 [s.sub.k ] is the estimated CIR 102 at the state s.sub.k, c.sub.i,k [s.sub.k-1 :s.sub.k.sup.sv ] is the estimated CIR at the state s.sub.k-1 on the transition of the survivor path to the state s.sub.k, .delta. is a step size parameter, and .cndot.* designates a complex conjugate.
The per-survivor processing MLSE exhibits a good bit error rate performance for a fast time-varying channel by carrying out the foregoing per-survivor channel estimation.
On the other hand, an increasing number of states are required to implement the MLSE that can equalize the delay waves with long delay time on a channel with large delay spread, and this makes the device too bulky. In view of this, a list-output Viterbi equalizer using list-output Viterbi algorithm is proposed conventionally to restrain the device scale. The list-output Viterbi algorithm is disclosed in T. Hashimoto, "A List-Type Reduced-Constraint Generalization of the Viterbi Algorithm", IEEE Transactions on Information Theory, Vol. 33, No. 6, 1987, pp. 866-876 (called REF. 2 from now on). It generalizes the Viterbi algorithm by the following steps (a) and (b).
(a) Setting the memory length of the Viterbi algorithm smaller than the constraint length L of a channel or of a code; and PA1 (b) Increasing the number of survivor paths connected to respective states to S rather than one, where S is a positive integer.
The generalization concept (a) is the same as that of the decision feedback sequence estimation (DFSE). On the other hand, the generalization concept (b) is to select S paths with most likely metrics from among 2S connected paths in the case of binary transmission, for example.
The conventional list-output Viterbi equalizer using this list-output Viterbi algorithm can limit the degradation from a performance of the MLSE to a certain level with a rather small device size by leaving a plurality of survivor paths at respective states.
A configuration and operation of the list-output Viterbi equalizer will now be described as the second conventional example.
FIG. 3 is a block diagram showing a configuration of the conventional list-output Viterbi equalizer disclosed in the REF. 2, for example. In FIG. 3, the reference numeral 31 designates a branch metric generator; 32 designates an ACS operation circuit; 33 designates a path metric memory; 34 designates a path memory; 301 designates a received baseband signal; 302 designates an estimated CIR; 303 designates survivor paths connected to a state; 304 designates branch metrics; 305 designates path metrics; 306 designates path metrics at previous timing; 307 designates survivor paths; and 308 designates hard decision data.
Next, the operation of the second conventional device will be described.
Here, we define a u th path s.sub.k [u] connected to a state s.sub.k and a vth path s.sub.k /s.sub.k-1 [v] connected to a branch s.sub.k /s.sub.k-1 as the following expressions (8) and (9). EQU s.sub.k [u]=[I.sub.k, I.sub.k-1, . . . ,I.sub.k-V-1, I.sub.k-V.sup.sv, . . . , I.sub.k-L.sup.sv, . . . ] (8) EQU s.sub.k /s.sub.k-1 [v]=[I.sub.k, I.sub.k-1, . . . I.sub.k-V, I.sub.k-V-1, . . . ,I.sub.k-L.sup.sv, . . . ] (9)
where, I.sub.k.sup.sv is a candidate of the transmitted sequence based on the uth or vth survivor path connected to the state s.sub.k or to the branch s.sub.k /s.sub.k-1.
The branch metric generator 31, receiving the received baseband signal 301, estimated CIR 302 and survivor paths 303 connected to the state, compares the received baseband signal 301 with the replicas of the received signal obtained from the estimated CIR 302 and survivor paths connected to the state, and generates the branch metrics 304 for all the branch candidates s.sub.k /s.sub.k-1 [v](v=1,2, . . . ,S) to be supplied to the ACS operation circuit 32. Using the squared Euclidean distance as a metric criteria, the branch metrics 304 can be expressed by the following equations (10) and (11).
.GAMMA..sub.k [s.sub.k /s.sub.k-1 [v]]=.vertline.r.sub.k -r.sub.k [s.sub.k /s.sub.k-1 [v]].vertline..sup.2 (10)
##EQU3##
where .GAMMA..sub.k [s.sub.k /s.sub.k-1 [v]] is the branch metric 304 of the branch s.sub.k /s.sub.k-1 [v], r.sub.k is the received baseband signal 301, r.sub.k [s.sub.k /s.sub.k-1 [v]] is the replica of the received signal determined by the branch s.sub.k /s.sub.k-1 [v], c.sub.i is the estimated CIR 302, L is the channel memory length, and V is the memory length of the Viterbi algorithm.
The ACS operation circuit 32 adds the branch metrics 304 to. the path metrics at previous timing 306 stored in the path metric memory 33 as in expression (12), and calculates the path metric candidates for all the branch candidates s.sub.k /s.sub.k-1 [v](v=1,2, . . . ,S). EQU H.sub.k [s.sub.k /s.sub.k-1 [v]]=H.sub.k-1 [s.sub.k-1 [v]]+.GAMMA..sub.k [s.sub.k /s.sub.k-1 [v]] (12)
where, H.sub.k [s.sub.k /s.sub.k-1 [v]] is a path metric candidate determined by the branch s.sub.k /s.sub.k-1 [v], and H.sub.k-1 [s.sub.k-1 [v]] is the path metric at previous timing 306 determined by the state s.sub.k-1 [v]. The ACS operation circuit 32 further carries out the processing as shown by the following equation (13) for each of all the states s.sub.k [u](u=1,2, . . . ,S) in all orders. Specifically, the ACS operation circuit 32 selects the uth smallest candidates from among the path metric candidates H.sub.k [s.sub.k /s.sub.k-1 [v]] determined by the branch s.sub.k /s.sub.k-1 [v] connected to the state s.sub.k, and supplies them to the path metric memory 33 as the path metrics 305. ##EQU4##
Here, H.sub.k [s.sub.k [u]] is the path metric 305 determined by the state s.sub.k [u]. The ACS operation circuit 32 also supplies the path memory 34 with the information on the selected paths as the survivor paths 307.
The path memory 34 stores the survivor paths 307 for a predetermined time period, traces the paths whose path metrics at previous timing 306 are smallest, and outputs the transmitted sequence determined by the paths as the hard decision data 308.
As described above, the conventional list-output Viterbi equalizer exhibits a good bit error rate performance in a considerable small size even for a channel with rather large delay spread by leaving a plurality of paths for each state. In addition, utilizing the diversity effect of the delay waves in the case of large delay spread, the adaptive configuration that also carries out the CIR estimation for data section can constrain the degradation in the bit error rate performance to some extent for a channel with fast time-varying fading.
However, the conventional per-survivor processing MLSE and list-output Viterbi equalizer with the foregoing configurations have the following problems. First, the per-survivor processing MLSE requires a considerably large device scale for a channel with large delay spread. Second, the list-output Viterbi equalizer degrades the bit error rate performance for a channel with small delay spread and fast time-varying fading.
The present invention is implemented to solve the foregoing problems. Therefore, an object of the present invention is to provide an adaptive equalizer and adaptive equalization scheme capable of achieving a good bit error rate performance for both the channel with large delay spread and the channel with small delay spread and fast time-varying fading.